Open AccessMulti‐objective genetic algorithm on hexagonal search for fast motion estimation
Author(s) -
Palaniraj Palanivel,
Sakthivel Ganesan
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5918
Subject(s) - genetic algorithm , computer science , algorithm , selection (genetic algorithm) , histogram , peak signal to noise ratio , block (permutation group theory) , chromosome , frame (networking) , search algorithm , process (computing) , mathematical optimization , artificial intelligence , mathematics , pattern recognition (psychology) , image (mathematics) , machine learning , gene , telecommunications , biochemistry , chemistry , geometry , operating system
Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10–15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS‐GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS‐GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to ∼316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.